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    Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type

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    salminen_et_al_2019.pdf (584.9Kb)
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    Publication date
    2019-08
    Author
    Salminen, J.
    Yoganathan, Vignesh
    Corporan, J.
    Jansen, B.J.
    Jung, S.-G.
    Keyword
    Machine learning
    Auto-tagging
    Web content
    Content marketing
    Neural network
    Digital marketing
    Rights
    © 2019 Elsevier. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
    Peer-Reviewed
    Yes
    
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    Abstract
    As complex data becomes the norm, greater understanding of machine learning (ML) applications is needed for content marketers. Unstructured data, scattered across platforms in multiple forms, impedes performance and user experience. Automated classification offers a solution to this. We compare three state-of-the-art ML techniques for multilabel classification - Random Forest, K-Nearest Neighbor, and Neural Network - to automatically tag and classify online news articles. Neural Network performs the best, yielding an F1 Score of 70% and provides satisfactory cross-platform applicability on the same organisation's YouTube content. The developed model can automatically label 99.6% of the unlabelled website and 96.1% of the unlabelled YouTube content. Thus, we contribute to marketing literature via comparative evaluation of ML models for multilabel content classification, and cross-channel validation for a different type of content. Results suggest that organisations may optimise ML to auto-tag content across various platforms, opening avenues for aggregated analyses of content performance.
    URI
    http://hdl.handle.net/10454/17058
    Version
    Accepted manuscript
    Citation
    Salminen J, Yoganathan V, Corporan J et al (2019) Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type. Journal of Business Research. 101: 203-217.
    Link to publisher’s version
    https://doi.org/10.1016/j.jbusres.2019.04.018
    Type
    Article
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    Management and Law Publications

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